






import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

							if is_vision_available():
														from transformers import OneFormerImageProcessor
														from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
														from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
							from PIL import Image






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE="shi-labs/oneformer_demo"				)     ->    Optional[int]:
							with open(hf_hub_download(_lowerCamelCase     ,     _lowerCamelCase     ,     repo_type='dataset'				)     ,     'r'				) as f:
														_lowercase							:   Optional[Any] 							=   json.load(_lowerCamelCase				)
							_lowercase							:   Optional[Any] 							=   {}
							_lowercase							:   int 							=   []
							_lowercase							:   List[str] 							=   []
							for key, info in class_info.items():
														_lowercase							:   List[Any] 							=   info['name']
														class_names.append(info['name']				)
														if info["isthing"]:
																					thing_ids.append(int(_lowerCamelCase				)				)
							_lowercase							:   List[str] 							=   thing_ids
							_lowercase							:   Any 							=   class_names
							return metadata

class 				lowerCAmelCase_	(		unittest.TestCase			):


				def __init__(					self ,						_lowerCAmelCase ,						_lowerCAmelCase=7 ,						_lowerCAmelCase=3 ,						_lowerCAmelCase=3_0 ,						_lowerCAmelCase=4_0_0 ,						_lowerCAmelCase=None ,						_lowerCAmelCase=True ,						_lowerCAmelCase=True ,						_lowerCAmelCase=[0.5, 0.5, 0.5] ,						_lowerCAmelCase=[0.5, 0.5, 0.5] ,						_lowerCAmelCase=1_0 ,						_lowerCAmelCase=False ,						_lowerCAmelCase=2_5_5 ,						_lowerCAmelCase="shi-labs/oneformer_demo" ,						_lowerCAmelCase="ade20k_panoptic.json" ,						_lowerCAmelCase=1_0 ,						):
											_lowercase							:   Optional[int] 							=   parent
											_lowercase							:   Optional[int] 							=   batch_size
											_lowercase							:   Tuple 							=   num_channels
											_lowercase							:   str 							=   min_resolution
											_lowercase							:   List[str] 							=   max_resolution
											_lowercase							:   Optional[Any] 							=   do_resize
											_lowercase							:   int 							=   {'shortest_edge': 3_2, 'longest_edge': 1_3_3_3} if size is None else size
											_lowercase							:   Union[str, Any] 							=   do_normalize
											_lowercase							:   Union[str, Any] 							=   image_mean
											_lowercase							:   Optional[Any] 							=   image_std
											_lowercase							:   Any 							=   class_info_file
											_lowercase							:   int 							=   prepare_metadata(__SCREAMING_SNAKE_CASE ,						__SCREAMING_SNAKE_CASE      )
											_lowercase							:   Union[str, Any] 							=   num_text
											_lowercase							:   str 							=   repo_path

											# for the post_process_functions
											_lowercase							:   Tuple 							=   2
											_lowercase							:   Optional[Any] 							=   1_0
											_lowercase							:   List[Any] 							=   1_0
											_lowercase							:   Tuple 							=   3
											_lowercase							:   Any 							=   4
											_lowercase							:   Tuple 							=   num_labels
											_lowercase							:   Optional[int] 							=   do_reduce_labels
											_lowercase							:   List[Any] 							=   ignore_index


				def 						__a					(					self      ):
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "num_labels": self.num_labels,
											    "do_reduce_labels": self.do_reduce_labels,
											    "ignore_index": self.ignore_index,
											    "class_info_file": self.class_info_file,
											    "metadata": self.metadata,
											    "num_text": self.num_text,
											}


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase=False      ):
											if not batched:
																		_lowercase							:   List[Any] 							=   image_inputs[0]
																		if isinstance(__SCREAMING_SNAKE_CASE ,						Image.Image      ):
																									_lowercase							,							_lowercase							:   Tuple 							=   image.size
																		else:
																									_lowercase							,							_lowercase							:   Union[str, Any] 							=   image.shape[1], image.shape[2]
																		if w < h:
																									_lowercase							:   Any 							=   int(self.size['shortest_edge'] * h / w      )
																									_lowercase							:   Union[str, Any] 							=   self.size['shortest_edge']
																		elif w > h:
																									_lowercase							:   Any 							=   self.size['shortest_edge']
																									_lowercase							:   List[Any] 							=   int(self.size['shortest_edge'] * w / h      )
																		else:
																									_lowercase							:   List[str] 							=   self.size['shortest_edge']
																									_lowercase							:   Optional[int] 							=   self.size['shortest_edge']

											else:
																		_lowercase							:   Tuple 							=   []
																		for image in image_inputs:
																									_lowercase							,							_lowercase							:   List[str] 							=   self.get_expected_values([image]      )
																									expected_values.append((expected_height, expected_width)      )
																		_lowercase							:   str 							=   max(__SCREAMING_SNAKE_CASE ,						key=lambda _lowerCAmelCase      : item[0]      )[0]
																		_lowercase							:   Tuple 							=   max(__SCREAMING_SNAKE_CASE ,						key=lambda _lowerCAmelCase      : item[1]      )[1]

											return expected_height, expected_width


				def 						__a					(					self      ):
											return OneFormerForUniversalSegmentationOutput(
											    # +1 for null class
											    class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)      ) ,						masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)      ) ,						)




@require_torch
@require_vision
class 				lowerCAmelCase_	(		__snake_case   ,		unittest.TestCase			):
				_UpperCamelCase					:       Dict							       = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
				# only for test_image_processing_common.test_image_proc_to_json_string
				_UpperCamelCase					:       Tuple							       = image_processing_class


				def 						__a					(					self      ):
											_lowercase							:   str 							=   OneFormerImageProcessorTester(self      )


				@property
				def 						__a					(					self      ):
											return self.image_processing_tester.prepare_image_processor_dict()


				def 						__a					(					self      ):
											_lowercase							:   Tuple 							=   self.image_processing_class(**self.image_processor_dict      )
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,						'image_mean'      )      )
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,						'image_std'      )      )
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,						'do_normalize'      )      )
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,						'do_resize'      )      )
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,						'size'      )      )
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,						'ignore_index'      )      )
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,						'class_info_file'      )      )
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,						'num_text'      )      )
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,						'repo_path'      )      )
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,						'metadata'      )      )
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,						'do_reduce_labels'      )      )


				def 						__a					(					self      ):
											pass


				def 						__a					(					self      ):
											# Initialize image_processor
											_lowercase							:   Union[str, Any] 							=   self.image_processing_class(**self.image_processor_dict      )
											# create random PIL images
											_lowercase							:   Optional[Any] 							=   prepare_image_inputs(self.image_processing_tester ,						equal_resolution=__SCREAMING_SNAKE_CASE      )
											for image in image_inputs:
																		self.assertIsInstance(__SCREAMING_SNAKE_CASE ,						Image.Image      )

											# Test not batched input
											_lowercase							:   Union[str, Any] 							=   image_processor(image_inputs[0] ,						['semantic'] ,						return_tensors='pt'      ).pixel_values

											_lowercase							,							_lowercase							:   Any 							=   self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE      )

											self.assertEqual(
											    encoded_images.shape ,						(1, self.image_processing_tester.num_channels, expected_height, expected_width) ,						)

											# Test batched
											_lowercase							,							_lowercase							:   str 							=   self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE ,						batched=__SCREAMING_SNAKE_CASE      )

											_lowercase							:   str 							=   image_processor(
											    __SCREAMING_SNAKE_CASE ,						['semantic'] * len(__SCREAMING_SNAKE_CASE      ) ,						return_tensors='pt'      ).pixel_values
											self.assertEqual(
											    encoded_images.shape ,						(
											        self.image_processing_tester.batch_size,
											        self.image_processing_tester.num_channels,
											        expected_height,
											        expected_width,
											    ) ,						)


				def 						__a					(					self      ):
											# Initialize image_processor
											_lowercase							:   Dict 							=   self.image_processing_class(**self.image_processor_dict      )
											# create random numpy tensors
											_lowercase							:   Union[str, Any] 							=   prepare_image_inputs(self.image_processing_tester ,						equal_resolution=__SCREAMING_SNAKE_CASE ,						numpify=__SCREAMING_SNAKE_CASE      )
											for image in image_inputs:
																		self.assertIsInstance(__SCREAMING_SNAKE_CASE ,						np.ndarray      )

											# Test not batched input
											_lowercase							:   Optional[Any] 							=   image_processor(image_inputs[0] ,						['semantic'] ,						return_tensors='pt'      ).pixel_values

											_lowercase							,							_lowercase							:   Union[str, Any] 							=   self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE      )

											self.assertEqual(
											    encoded_images.shape ,						(1, self.image_processing_tester.num_channels, expected_height, expected_width) ,						)

											# Test batched
											_lowercase							,							_lowercase							:   Dict 							=   self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE ,						batched=__SCREAMING_SNAKE_CASE      )

											_lowercase							:   Dict 							=   image_processor(
											    __SCREAMING_SNAKE_CASE ,						['semantic'] * len(__SCREAMING_SNAKE_CASE      ) ,						return_tensors='pt'      ).pixel_values
											self.assertEqual(
											    encoded_images.shape ,						(
											        self.image_processing_tester.batch_size,
											        self.image_processing_tester.num_channels,
											        expected_height,
											        expected_width,
											    ) ,						)


				def 						__a					(					self      ):
											# Initialize image_processor
											_lowercase							:   Union[str, Any] 							=   self.image_processing_class(**self.image_processor_dict      )
											# create random PyTorch tensors
											_lowercase							:   List[Any] 							=   prepare_image_inputs(self.image_processing_tester ,						equal_resolution=__SCREAMING_SNAKE_CASE ,						torchify=__SCREAMING_SNAKE_CASE      )
											for image in image_inputs:
																		self.assertIsInstance(__SCREAMING_SNAKE_CASE ,						torch.Tensor      )

											# Test not batched input
											_lowercase							:   Dict 							=   image_processor(image_inputs[0] ,						['semantic'] ,						return_tensors='pt'      ).pixel_values

											_lowercase							,							_lowercase							:   Optional[Any] 							=   self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE      )

											self.assertEqual(
											    encoded_images.shape ,						(1, self.image_processing_tester.num_channels, expected_height, expected_width) ,						)

											# Test batched
											_lowercase							,							_lowercase							:   Optional[Any] 							=   self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE ,						batched=__SCREAMING_SNAKE_CASE      )

											_lowercase							:   Any 							=   image_processor(
											    __SCREAMING_SNAKE_CASE ,						['semantic'] * len(__SCREAMING_SNAKE_CASE      ) ,						return_tensors='pt'      ).pixel_values
											self.assertEqual(
											    encoded_images.shape ,						(
											        self.image_processing_tester.batch_size,
											        self.image_processing_tester.num_channels,
											        expected_height,
											        expected_width,
											    ) ,						)


				def 						__a					(					self ,						_lowerCAmelCase=False ,						_lowerCAmelCase=False ,						_lowerCAmelCase="np"      ):
											_lowercase							:   int 							=   self.image_processing_class(**self.image_processor_dict      )
											# prepare image and target
											_lowercase							:   int 							=   self.image_processing_tester.num_labels
											_lowercase							:   Optional[int] 							=   None
											_lowercase							:   int 							=   None
											_lowercase							:   Union[str, Any] 							=   prepare_image_inputs(self.image_processing_tester ,						equal_resolution=__SCREAMING_SNAKE_CASE      )
											if with_segmentation_maps:
																		_lowercase							:   Optional[Any] 							=   num_labels
																		if is_instance_map:
																									_lowercase							:   Tuple 							=   list(range(__SCREAMING_SNAKE_CASE      )      ) * 2
																									_lowercase							:   Dict 							=   dict(enumerate(__SCREAMING_SNAKE_CASE      )      )
																		_lowercase							:   List[Any] 							=   [
																		    np.random.randint(0 ,						high * 2 ,						(img.size[1], img.size[0])      ).astype(np.uinta      ) for img in image_inputs
																		]
																		if segmentation_type == "pil":
																									_lowercase							:   str 							=   [Image.fromarray(__SCREAMING_SNAKE_CASE      ) for annotation in annotations]

											_lowercase							:   List[Any] 							=   image_processor(
											    __SCREAMING_SNAKE_CASE ,						['semantic'] * len(__SCREAMING_SNAKE_CASE      ) ,						__SCREAMING_SNAKE_CASE ,						return_tensors='pt' ,						instance_id_to_semantic_id=__SCREAMING_SNAKE_CASE ,						pad_and_return_pixel_mask=__SCREAMING_SNAKE_CASE ,						)

											return inputs


				def 						__a					(					self      ):
											pass


				def 						__a					(					self      ):
											def common(_lowerCAmelCase=False ,						_lowerCAmelCase=None      ):
																		_lowercase							:   Any 							=   self.comm_get_image_processor_inputs(
																		    with_segmentation_maps=__SCREAMING_SNAKE_CASE ,						is_instance_map=__SCREAMING_SNAKE_CASE ,						segmentation_type=__SCREAMING_SNAKE_CASE      )

																		_lowercase							:   List[str] 							=   inputs['mask_labels']
																		_lowercase							:   str 							=   inputs['class_labels']
																		_lowercase							:   Optional[int] 							=   inputs['pixel_values']
																		_lowercase							:   int 							=   inputs['text_inputs']

																		# check the batch_size
																		for mask_label, class_label, text_input in zip(__SCREAMING_SNAKE_CASE ,						__SCREAMING_SNAKE_CASE ,						__SCREAMING_SNAKE_CASE      ):
																									self.assertEqual(mask_label.shape[0] ,						class_label.shape[0]      )
																									# this ensure padding has happened
																									self.assertEqual(mask_label.shape[1:] ,						pixel_values.shape[2:]      )
																									self.assertEqual(len(__SCREAMING_SNAKE_CASE      ) ,						self.image_processing_tester.num_text      )

											common()
											common(is_instance_map=__SCREAMING_SNAKE_CASE      )
											common(is_instance_map=__SCREAMING_SNAKE_CASE ,						segmentation_type='pil'      )
											common(is_instance_map=__SCREAMING_SNAKE_CASE ,						segmentation_type='pil'      )


				def 						__a					(					self      ):
											_lowercase							:   Any 							=   np.zeros((2_0, 5_0)      )
											_lowercase							:   Optional[int] 							=   1
											_lowercase							:   Optional[Any] 							=   1
											_lowercase							:   Any 							=   1

											_lowercase							:   str 							=   binary_mask_to_rle(__SCREAMING_SNAKE_CASE      )
											self.assertEqual(len(__SCREAMING_SNAKE_CASE      ) ,						4      )
											self.assertEqual(rle[0] ,						2_1      )
											self.assertEqual(rle[1] ,						4_5      )


				def 						__a					(					self      ):
											_lowercase							:   Dict 							=   self.image_processing_class(
											    num_labels=self.image_processing_tester.num_classes ,						max_seq_length=7_7 ,						task_seq_length=7_7 ,						class_info_file='ade20k_panoptic.json' ,						num_text=self.image_processing_tester.num_text ,						repo_path='shi-labs/oneformer_demo' ,						)
											_lowercase							:   Optional[int] 							=   self.image_processing_tester.get_fake_oneformer_outputs()

											_lowercase							:   Any 							=   fature_extractor.post_process_semantic_segmentation(__SCREAMING_SNAKE_CASE      )

											self.assertEqual(len(__SCREAMING_SNAKE_CASE      ) ,						self.image_processing_tester.batch_size      )
											self.assertEqual(
											    segmentation[0].shape ,						(
											        self.image_processing_tester.height,
											        self.image_processing_tester.width,
											    ) ,						)

											_lowercase							:   Dict 							=   [(1, 4) for i in range(self.image_processing_tester.batch_size      )]
											_lowercase							:   str 							=   fature_extractor.post_process_semantic_segmentation(__SCREAMING_SNAKE_CASE ,						target_sizes=__SCREAMING_SNAKE_CASE      )

											self.assertEqual(segmentation[0].shape ,						target_sizes[0]      )


				def 						__a					(					self      ):
											_lowercase							:   Any 							=   self.image_processing_class(
											    num_labels=self.image_processing_tester.num_classes ,						max_seq_length=7_7 ,						task_seq_length=7_7 ,						class_info_file='ade20k_panoptic.json' ,						num_text=self.image_processing_tester.num_text ,						repo_path='shi-labs/oneformer_demo' ,						)
											_lowercase							:   Tuple 							=   self.image_processing_tester.get_fake_oneformer_outputs()
											_lowercase							:   List[Any] 							=   image_processor.post_process_instance_segmentation(__SCREAMING_SNAKE_CASE ,						threshold=0      )

											self.assertTrue(len(__SCREAMING_SNAKE_CASE      ) == self.image_processing_tester.batch_size      )
											for el in segmentation:
																		self.assertTrue('segmentation' in el      )
																		self.assertTrue('segments_info' in el      )
																		self.assertEqual(type(el['segments_info']      ) ,						__SCREAMING_SNAKE_CASE      )
																		self.assertEqual(
																		    el['segmentation'].shape ,						(self.image_processing_tester.height, self.image_processing_tester.width)      )


				def 						__a					(					self      ):
											_lowercase							:   str 							=   self.image_processing_class(
											    num_labels=self.image_processing_tester.num_classes ,						max_seq_length=7_7 ,						task_seq_length=7_7 ,						class_info_file='ade20k_panoptic.json' ,						num_text=self.image_processing_tester.num_text ,						repo_path='shi-labs/oneformer_demo' ,						)
											_lowercase							:   str 							=   self.image_processing_tester.get_fake_oneformer_outputs()
											_lowercase							:   Optional[Any] 							=   image_processor.post_process_panoptic_segmentation(__SCREAMING_SNAKE_CASE ,						threshold=0      )

											self.assertTrue(len(__SCREAMING_SNAKE_CASE      ) == self.image_processing_tester.batch_size      )
											for el in segmentation:
																		self.assertTrue('segmentation' in el      )
																		self.assertTrue('segments_info' in el      )
																		self.assertEqual(type(el['segments_info']      ) ,						__SCREAMING_SNAKE_CASE      )
																		self.assertEqual(
																		    el['segmentation'].shape ,						(self.image_processing_tester.height, self.image_processing_tester.width)      )












"""simple docstring"""





import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class 	a		(   __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					DebertaTokenizer
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					True
    SCREAMING_SNAKE_CASE		:	Any		=					DebertaTokenizerFast






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Dict:
      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      lowerCamelCase_       	=		[
          'l',
          'o',
          'w',
          'e',
          'r',
          's',
          't',
          'i',
          'd',
          'n',
          '\u0120',
          '\u0120l',
          '\u0120n',
          '\u0120lo',
          '\u0120low',
          'er',
          '\u0120lowest',
          '\u0120newer',
          '\u0120wider',
          '[UNK]',
      ]
      lowerCamelCase_       	=		dict(zip(__SCREAMING_SNAKE_CASE			,		range(len(__SCREAMING_SNAKE_CASE   )   )   )   )
      lowerCamelCase_       	=		['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
      lowerCamelCase_       	=		{'unk_token': '[UNK]'}

      lowerCamelCase_       	=		os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES['vocab_file']   )
      lowerCamelCase_       	=		os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES['merges_file']   )
      with open(self.vocab_file			,		'w'			,		encoding='utf-8'   ) as fp:
        fp.write(json.dumps(__SCREAMING_SNAKE_CASE   ) + '\n'   )
      with open(self.merges_file			,		'w'			,		encoding='utf-8'   ) as fp:
        fp.write('\n'.join(__SCREAMING_SNAKE_CASE   )   )






    def   UpperCamelCase			(			self	:				Tuple			,		**__SCREAMING_SNAKE_CASE	:				Dict   )	->						List[Any]:
      kwargs.update(self.special_tokens_map   )
      return self.tokenizer_class.from_pretrained(self.tmpdirname			,		**__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				str			,		__SCREAMING_SNAKE_CASE	:				Any   )	->						Dict:
      lowerCamelCase_       	=		'lower newer'
      lowerCamelCase_       	=		'lower newer'
      return input_text, output_text






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Optional[Any]:
      lowerCamelCase_       	=		self.get_tokenizer()
      lowerCamelCase_       	=		'lower newer'
      lowerCamelCase_       	=		['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
      lowerCamelCase_       	=		tokenizer.tokenize(__SCREAMING_SNAKE_CASE   )
      self.assertListEqual(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokens + [tokenizer.unk_token]
      lowerCamelCase_       	=		[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE   )			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Tuple:
      lowerCamelCase_       	=		self.get_tokenizer()
      lowerCamelCase_       	=		tokenizer('Hello'			,		'World'   )
      lowerCamelCase_       	=		[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
      self.assertListEqual(tokd['token_type_ids']			,		__SCREAMING_SNAKE_CASE   )






    @slow
    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Tuple:
      lowerCamelCase_       	=		self.tokenizer_class.from_pretrained('microsoft/deberta-base'   )

      lowerCamelCase_       	=		tokenizer.encode('sequence builders'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.encode('multi-sequence build'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokenizer.encode(
          'sequence builders'			,		add_special_tokens=__SCREAMING_SNAKE_CASE			,		add_prefix_space=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.encode(
          'sequence builders'			,		'multi-sequence build'			,		add_special_tokens=__SCREAMING_SNAKE_CASE			,		add_prefix_space=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      assert encoded_sentence == encoded_text_from_decode
      assert encoded_pair == encoded_pair_from_decode






    @slow
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Optional[Any]:
      lowerCamelCase_       	=		[self.tokenizer_class]
      if self.test_rust_tokenizer:
        tokenizer_classes.append(self.rust_tokenizer_class   )

      for tokenizer_class in tokenizer_classes:
        lowerCamelCase_       	=		tokenizer_class.from_pretrained('microsoft/deberta-base'   )

        lowerCamelCase_       	=		[
            'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
            'ALBERT incorporates two parameter reduction techniques',
            'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
            ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
            ' vocabulary embedding.',
        ]

        lowerCamelCase_       	=		tokenizer(__SCREAMING_SNAKE_CASE			,		padding=__SCREAMING_SNAKE_CASE   )
        lowerCamelCase_       	=		[tokenizer.decode(__SCREAMING_SNAKE_CASE			,		skip_special_tokens=__SCREAMING_SNAKE_CASE   ) for seq in encoding['input_ids']]

        # fmt: off
        lowerCamelCase_       	=		{
            'input_ids': [
                [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
            ],
            'token_type_ids': [
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
            ],
            'attention_mask': [
                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
            ]
        }
        # fmt: on

        lowerCamelCase_       	=		[
            'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
            'ALBERT incorporates two parameter reduction techniques',
            'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
            ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
            ' vocabulary embedding.',
        ]

        self.assertDictEqual(encoding.data			,		__SCREAMING_SNAKE_CASE   )

        for expected, decoded in zip(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
          self.assertEqual(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )




